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---
pretty_name: Tracks
license: cc-by-4.0
tags:
- computer-vision
- human-motion
- robotics
- trajectory
- pose-estimation
- navigation
- retail
task_categories:
- image-feature-extraction
- keypoint-detection
- object-detection
- image-segmentation
- reinforcement-learning
size_categories:
- 1M<n<100M
---
# Tracks Dataset
**Real Human Motion for Robotics Planning and Simulation**
**The ros2 docker container compiliation and visualization script instructions can be found here: https://huggingface.co/datasets/standard-cognition/Tracks/blob/main/keypoint-db/README.md**
---
## Overview
<video src="https://huggingface.co/datasets/standard-cognition/Tracks/resolve/main/assets/visual.mp4"
autoplay
loop
muted
playsinline
style="width:100%;height:auto;border-radius:8px;">
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</video>
The **Tracks Dataset** captures continuous, real-world human movement in retail environments, providing one of the largest and most structured pose-based trajectory corpora available for **robotics** and **embodied AI** research.
Each record represents **3D pose sequences** sampled at 10 Hz across normalized store coordinates, enabling research in motion planning, human-aware navigation, and humanoid gait learning derived directly from real behavior :contentReference[oaicite:0]{index=0}.
---
## Key Specifications
| Field | Description |
|:------|:-------------|
| **Source** | Anonymized in-store multi-camera captures (10 retail sites) |
| **Scope** | ≈ 60,000 hours of human trajectory data (plus 1-hour evaluation subset) |
| **Format** | CSV schema, ROS 2–compatible via playback plug-in |
| **Sampling Frequency** | 10 Hz (10 FPS) |
| **Pose Structure** | 26 keypoints per person per frame (3D coordinates) |
| **Environment** | Real retail environments with normalized floor layouts |
| **Evaluation Subset** | One-hour segment including trajectories + store layout |
| **Key Metrics** | ≈ 2.3 M unique shoppers |
| **Anonymization** | Face and body suppression; coordinate-only representation |
| **Governance** | Managed under Standard AI’s data governance policies aligned with GDPR/CCPA and Responsible AI principles |
---
## Integration & Applications
- Distributed in **CSV** with schema documentation and import notebooks.
- Ready for **ROS 2** integration for **path planning** and **human–robot interaction** simulation.
- Compatible with **Python**, **PyTorch**, and standard **reinforcement-learning** frameworks.
### Example Research Uses
- Motion prediction and trajectory planning
- Reinforcement learning for humanoid gait and control
- Human-aware navigation and avoidance behavior
- Simulation of human–robot interaction environments
---
## Access
The **Tracks Dataset** is available now for evaluation and licensing.
- **Evaluation subset:** 1-hour sample under 30-day Evaluation Agreement (private Hugging Face repo).
- **Full dataset:** 60,000-hour commercial dataset available by request.
For inquiries or licensing:
✉️ [[email protected]](mailto:[email protected])
---
## Citation
```bibtex
@dataset{standardlabs_tracks_2025,
title = {Tracks Dataset: Real Human Motion for Robotics Planning and Simulation},
author = {Standard Labs},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/standard-labs/tracks}
}
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